{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "0b9ffbb2-8d33-eb0c-5a42-0733bdc3b74b",
    "_uuid": "e14f65723f5ac826104e861c45e58525740d8b2a"
   },
   "source": [
    "# Bikeshare数据集上的数据探索\n",
    "\n",
    "1、\t任务描述\n",
    "请在Capital Bikeshare （美国Washington, D.C.的一个共享单车公司）提供的自行车数据上进行回归分析。根据每天的天气信息，预测该天的单车共享骑行量。\n",
    "\n",
    "原始数据集地址：http://archive.ics.uci.edu/ml/datasets/Bike+Sharing+Dataset\n",
    "1)\t文件说明\n",
    "day.csv: 按天计的单车共享次数（作业只需使用该文件）\n",
    "hour.csv: 按小时计的单车共享次数（无需理会）\n",
    "readme：数据说明文件\n",
    "\n",
    "2)\t字段说明\n",
    "Instant记录号\n",
    "Dteday：日期\n",
    "Season：季节（1=春天、2=夏天、3=秋天、4=冬天）\n",
    "yr：年份，(0: 2011, 1:2012)\n",
    "mnth：月份( 1 to 12)\n",
    "hr：小时 (0 to 23)  （只在hour.csv有，作业忽略此字段）\n",
    "holiday：是否是节假日\n",
    "weekday：星期中的哪天，取值为0～6\n",
    "workingday：是否工作日\n",
    "1=工作日 （是否为工作日，1为工作日，0为非周末或节假日\n",
    "weathersit：天气（1：晴天，多云 ",
    "2：雾天，阴天 ",
    "3：小雪，小雨 ",
    "4：大雨，大雪，大雾）\n",
    "temp：气温摄氏度\n",
    "atemp：体感温度\n",
    "hum：湿度\n",
    "windspeed：风速\n",
    "casual：非注册用户个数\n",
    "registered：注册用户个数\n",
    "cnt：给定日期（天）时间（每小时）总租车人数，响应变量y （cnt = casual + registered）\n",
    "\n",
    "casual、registered和cnt三个特征均为要预测的y，作业里只需对cnt进行预测"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 导入必要的工具包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "2ba3154a-c2aa-3158-1984-63ad2c0c786a",
    "_uuid": "5eb696b95780825e94ddb49787f9fa339fc3833b"
   },
   "outputs": [],
   "source": [
    "# 数据读取及基本处理\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# plotting\n",
    "import seaborn as sn\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# setting params\n",
    "params = {'legend.fontsize': 'x-large',\n",
    "          'figure.figsize': (30, 10),\n",
    "          'axes.labelsize': 'x-large',\n",
    "          'axes.titlesize':'x-large',\n",
    "          'xtick.labelsize':'x-large',\n",
    "          'ytick.labelsize':'x-large'}\n",
    "\n",
    "sn.set_style('whitegrid')\n",
    "sn.set_context('talk')\n",
    "\n",
    "plt.rcParams.update(params)\n",
    "pd.options.display.max_colwidth = 600\n",
    "\n",
    "# pandas display data frames as tables\n",
    "from IPython.display import display, HTML"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读入数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "21fa35be-878b-b4f2-ef6e-68dc070b8bfa",
    "_uuid": "73aee228226be55c0c8a6e4fcbf818c56cd94926"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1        0        6           0   \n",
       "1        2  2011-01-02       1   0     1        0        0           0   \n",
       "2        3  2011-01-03       1   0     1        0        1           1   \n",
       "3        4  2011-01-04       1   0     1        0        2           1   \n",
       "4        5  2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读入数据\n",
    "train = pd.read_csv(\"day.csv\")\n",
    "train.head()\n",
    "#print(\"train : \" + str(train.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 731 entries, 0 to 730\n",
      "Data columns (total 16 columns):\n",
      "instant       731 non-null int64\n",
      "dteday        731 non-null object\n",
      "season        731 non-null int64\n",
      "yr            731 non-null int64\n",
      "mnth          731 non-null int64\n",
      "holiday       731 non-null int64\n",
      "weekday       731 non-null int64\n",
      "workingday    731 non-null int64\n",
      "weathersit    731 non-null int64\n",
      "temp          731 non-null float64\n",
      "atemp         731 non-null float64\n",
      "hum           731 non-null float64\n",
      "windspeed     731 non-null float64\n",
      "casual        731 non-null int64\n",
      "registered    731 non-null int64\n",
      "cnt           731 non-null int64\n",
      "dtypes: float64(4), int64(11), object(1)\n",
      "memory usage: 91.5+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "没有缺失数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "be11c1c7-acfe-cb9a-bc81-95d461b884c5",
    "_uuid": "b53750ea6a3a2a02aa4297f5401e29c2f4d92e31"
   },
   "source": [
    "## 数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>2.496580</td>\n",
       "      <td>0.500684</td>\n",
       "      <td>6.519836</td>\n",
       "      <td>0.028728</td>\n",
       "      <td>2.997264</td>\n",
       "      <td>0.683995</td>\n",
       "      <td>1.395349</td>\n",
       "      <td>0.495385</td>\n",
       "      <td>0.474354</td>\n",
       "      <td>0.627894</td>\n",
       "      <td>0.190486</td>\n",
       "      <td>848.176471</td>\n",
       "      <td>3656.172367</td>\n",
       "      <td>4504.348837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>211.165812</td>\n",
       "      <td>1.110807</td>\n",
       "      <td>0.500342</td>\n",
       "      <td>3.451913</td>\n",
       "      <td>0.167155</td>\n",
       "      <td>2.004787</td>\n",
       "      <td>0.465233</td>\n",
       "      <td>0.544894</td>\n",
       "      <td>0.183051</td>\n",
       "      <td>0.162961</td>\n",
       "      <td>0.142429</td>\n",
       "      <td>0.077498</td>\n",
       "      <td>686.622488</td>\n",
       "      <td>1560.256377</td>\n",
       "      <td>1937.211452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.059130</td>\n",
       "      <td>0.079070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022392</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>183.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.337083</td>\n",
       "      <td>0.337842</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.134950</td>\n",
       "      <td>315.500000</td>\n",
       "      <td>2497.000000</td>\n",
       "      <td>3152.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.498333</td>\n",
       "      <td>0.486733</td>\n",
       "      <td>0.626667</td>\n",
       "      <td>0.180975</td>\n",
       "      <td>713.000000</td>\n",
       "      <td>3662.000000</td>\n",
       "      <td>4548.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>548.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.655417</td>\n",
       "      <td>0.608602</td>\n",
       "      <td>0.730209</td>\n",
       "      <td>0.233214</td>\n",
       "      <td>1096.000000</td>\n",
       "      <td>4776.500000</td>\n",
       "      <td>5956.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.861667</td>\n",
       "      <td>0.840896</td>\n",
       "      <td>0.972500</td>\n",
       "      <td>0.507463</td>\n",
       "      <td>3410.000000</td>\n",
       "      <td>6946.000000</td>\n",
       "      <td>8714.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          instant      season          yr        mnth     holiday     weekday  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean   366.000000    2.496580    0.500684    6.519836    0.028728    2.997264   \n",
       "std    211.165812    1.110807    0.500342    3.451913    0.167155    2.004787   \n",
       "min      1.000000    1.000000    0.000000    1.000000    0.000000    0.000000   \n",
       "25%    183.500000    2.000000    0.000000    4.000000    0.000000    1.000000   \n",
       "50%    366.000000    3.000000    1.000000    7.000000    0.000000    3.000000   \n",
       "75%    548.500000    3.000000    1.000000   10.000000    0.000000    5.000000   \n",
       "max    731.000000    4.000000    1.000000   12.000000    1.000000    6.000000   \n",
       "\n",
       "       workingday  weathersit        temp       atemp         hum   windspeed  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean     0.683995    1.395349    0.495385    0.474354    0.627894    0.190486   \n",
       "std      0.465233    0.544894    0.183051    0.162961    0.142429    0.077498   \n",
       "min      0.000000    1.000000    0.059130    0.079070    0.000000    0.022392   \n",
       "25%      0.000000    1.000000    0.337083    0.337842    0.520000    0.134950   \n",
       "50%      1.000000    1.000000    0.498333    0.486733    0.626667    0.180975   \n",
       "75%      1.000000    2.000000    0.655417    0.608602    0.730209    0.233214   \n",
       "max      1.000000    3.000000    0.861667    0.840896    0.972500    0.507463   \n",
       "\n",
       "            casual   registered          cnt  \n",
       "count   731.000000   731.000000   731.000000  \n",
       "mean    848.176471  3656.172367  4504.348837  \n",
       "std     686.622488  1560.256377  1937.211452  \n",
       "min       2.000000    20.000000    22.000000  \n",
       "25%     315.500000  2497.000000  3152.000000  \n",
       "50%     713.000000  3662.000000  4548.000000  \n",
       "75%    1096.000000  4776.500000  5956.000000  \n",
       "max    3410.000000  6946.000000  8714.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#对数据值型特征，用常用统计量观察其分布\n",
    "train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. 离散特征的分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "season属性的不同取值和出现的次数\n",
      "3    188\n",
      "2    184\n",
      "1    181\n",
      "4    178\n",
      "Name: season, dtype: int64\n",
      "\n",
      "mnth属性的不同取值和出现的次数\n",
      "12    62\n",
      "10    62\n",
      "8     62\n",
      "7     62\n",
      "5     62\n",
      "3     62\n",
      "1     62\n",
      "11    60\n",
      "9     60\n",
      "6     60\n",
      "4     60\n",
      "2     57\n",
      "Name: mnth, dtype: int64\n",
      "\n",
      "weathersit属性的不同取值和出现的次数\n",
      "1    463\n",
      "2    247\n",
      "3     21\n",
      "Name: weathersit, dtype: int64\n",
      "\n",
      "weekday属性的不同取值和出现的次数\n",
      "6    105\n",
      "1    105\n",
      "0    105\n",
      "5    104\n",
      "4    104\n",
      "3    104\n",
      "2    104\n",
      "Name: weekday, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#对类别型特征，观察其取值范围及直方图\n",
    "categorical_features = ['season','mnth','weathersit','weekday']\n",
    "for col in categorical_features:\n",
    "    print '\\n%s属性的不同取值和出现的次数'%col\n",
    "    print train[col].value_counts()\n",
    "    train[col] = train[col].astype('object')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "类别型特征的取值不多\n",
    "类别型特征可以采用独热编码（One hot encoding）/哑编码"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. 数值特征的分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000001B019276198>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001B01AC00438>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000001B01AC2EDD8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001B01AC697B8>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#对数值型特征，直方图\n",
    "numerical_features = ['temp','atemp','hum','windspeed']\n",
    "train[numerical_features].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. 特征与目标之间的关系"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1 每年骑行量的分布\n",
    "violinplot中用x表示类别（年）信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b01adaa710>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.violinplot(data=train[['yr',\n",
    "                        'cnt']],\n",
    "              x=\"yr\",y=\"cnt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2011年和2012年的分布差异很大"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.2 一年中每天的骑车量\n",
    "用颜色参数hue表示类别（年）信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Invalid RGBA argument: masked",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-9-f3f59125519a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      9\u001b[0m                            'yr']],\n\u001b[0;32m     10\u001b[0m              \u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'dayofyear'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'cnt'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 11\u001b[1;33m              hue ='yr',ax=ax)\n\u001b[0m\u001b[0;32m     12\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"dayly distribution of counts\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\seaborn\\categorical.py\u001b[0m in \u001b[0;36mpointplot\u001b[1;34m(x, y, hue, data, order, hue_order, estimator, ci, n_boot, units, markers, linestyles, dodge, join, scale, orient, color, palette, errwidth, capsize, ax, **kwargs)\u001b[0m\n\u001b[0;32m   3338\u001b[0m         \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgca\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3339\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3340\u001b[1;33m     \u001b[0mplotter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3341\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3342\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\seaborn\\categorical.py\u001b[0m in \u001b[0;36mplot\u001b[1;34m(self, ax)\u001b[0m\n\u001b[0;32m   1809\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1810\u001b[0m         \u001b[1;34m\"\"\"Make the plot.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1811\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw_points\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1812\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mannotate_axes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1813\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0morient\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"h\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\seaborn\\categorical.py\u001b[0m in \u001b[0;36mdraw_points\u001b[1;34m(self, ax)\u001b[0m\n\u001b[0;32m   1805\u001b[0m                            \u001b[0mc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpoint_colors\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0medgecolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpoint_colors\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1806\u001b[0m                            \u001b[0mlinewidth\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmew\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmarker\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmarker\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmarkersize\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1807\u001b[1;33m                            zorder=z)\n\u001b[0m\u001b[0;32m   1808\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1809\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\__init__.py\u001b[0m in \u001b[0;36minner\u001b[1;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1587\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1588\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1589\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msanitize_sequence\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1590\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1591\u001b[0m         \u001b[0mbound\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnew_sig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36mscatter\u001b[1;34m(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, plotnonfinite, **kwargs)\u001b[0m\n\u001b[0;32m   4488\u001b[0m                 \u001b[0moffsets\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0moffsets\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4489\u001b[0m                 \u001b[0mtransOffset\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'transform'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtransData\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4490\u001b[1;33m                 \u001b[0malpha\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0malpha\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4491\u001b[0m                 )\n\u001b[0;32m   4492\u001b[0m         \u001b[0mcollection\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_transform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmtransforms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mIdentityTransform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\collections.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, paths, sizes, **kwargs)\u001b[0m\n\u001b[0;32m    881\u001b[0m         \"\"\"\n\u001b[0;32m    882\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 883\u001b[1;33m         \u001b[0mCollection\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    884\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_paths\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpaths\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    885\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_sizes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msizes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\collections.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, edgecolors, facecolors, linewidths, linestyles, capstyle, joinstyle, antialiaseds, offsets, transOffset, norm, cmap, pickradius, hatch, urls, offset_position, zorder, **kwargs)\u001b[0m\n\u001b[0;32m    126\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_hatch_color\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmcolors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_rgba\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmpl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrcParams\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'hatch.color'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    127\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_facecolor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfacecolors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 128\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_edgecolor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0medgecolors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    129\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_linewidth\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlinewidths\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    130\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_linestyle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlinestyles\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\collections.py\u001b[0m in \u001b[0;36mset_edgecolor\u001b[1;34m(self, c)\u001b[0m\n\u001b[0;32m    726\u001b[0m         \"\"\"\n\u001b[0;32m    727\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_original_edgecolor\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mc\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 728\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_set_edgecolor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    729\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    730\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mset_alpha\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\collections.py\u001b[0m in \u001b[0;36m_set_edgecolor\u001b[1;34m(self, c)\u001b[0m\n\u001b[0;32m    710\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    711\u001b[0m             \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 712\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_edgecolors\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmcolors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_rgba_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_alpha\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    713\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mset_hatch_color\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_edgecolors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    714\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_hatch_color\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_edgecolors\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\colors.py\u001b[0m in \u001b[0;36mto_rgba_array\u001b[1;34m(c, alpha)\u001b[0m\n\u001b[0;32m    284\u001b[0m     \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mempty\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m4\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    285\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcc\u001b[0m \u001b[1;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 286\u001b[1;33m         \u001b[0mresult\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mto_rgba\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    287\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    288\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\colors.py\u001b[0m in \u001b[0;36mto_rgba\u001b[1;34m(c, alpha)\u001b[0m\n\u001b[0;32m    175\u001b[0m         \u001b[0mrgba\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    176\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mrgba\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# Suppress exception chaining of cache lookup failure.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 177\u001b[1;33m         \u001b[0mrgba\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_to_rgba_no_colorcycle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    178\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    179\u001b[0m             \u001b[0m_colors_full_map\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcache\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrgba\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\colors.py\u001b[0m in \u001b[0;36m_to_rgba_no_colorcycle\u001b[1;34m(c, alpha)\u001b[0m\n\u001b[0;32m    236\u001b[0m         \u001b[1;31m# float)` and `np.array(...).astype(float)` all convert \"0.5\" to 0.5.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    237\u001b[0m         \u001b[1;31m# Test dimensionality to reject single floats.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 238\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Invalid RGBA argument: {!r}\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0morig_c\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    239\u001b[0m     \u001b[1;31m# Return a tuple to prevent the cached value from being modified.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    240\u001b[0m     \u001b[0mc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Invalid RGBA argument: masked"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import datetime\n",
    "\n",
    "train['date'] = pd.to_datetime(train['dteday'])\n",
    "train['dayofyear'] = train[\"date\"].dt.dayofyear  #减今年的第几天\n",
    "\n",
    "fig,ax = plt.subplots()\n",
    "sn.pointplot(data=train[['dayofyear',\n",
    "                           'cnt',\n",
    "                           'yr']],\n",
    "             x='dayofyear',y='cnt',\n",
    "             hue ='yr',ax=ax)\n",
    "ax.set(title=\"dayly distribution of counts\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "每年开始和结束的数量少，中间多，骑行量和季节/月份有关"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.3 季节与骑车数量的关系\n",
    "violinplot得到详细分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10b716850>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Jz3lXn50NdKlZHjhwYHbedKkxMuCTlFNCCNmALzRrnV3vPHySpK+zZMkS7rzz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zdH0q95utORREqt97ZGcDzHIiQkJL98F3GaMYZevXpx0UUX8fTTT3Pw4EHMaoP/oB+7lX2On/JF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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b716310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.violinplot(data=train[['season',\n",
    "                        'cnt']],\n",
    "              x=\"season\",y=\"cnt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "能看出来每个季节骑行量的分布不同\n",
    "barplot利用矩阵条的高度反映数值变量的集中趋势，以及使用errorbar功能（差棒图）来估计变量之间的差值统计。\n",
    "谨记barplot展示的是某种变量分布的平均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5,1,u'Monthly distribution of counts')]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1453dd10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "sn.barplot(data=train[['season',\n",
    "                       'cnt']],\n",
    "           x=\"season\",y=\"cnt\")\n",
    "ax.set(title=\"Seasonly distribution of counts\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "骑行量和季节的关系就很明显了：第2、3、4季度的骑行量明显高于第1季度"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.4 月份与骑车数量的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5,1,u'Monthly distribution of counts')]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a18792290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "sn.barplot(data=train[['mnth',\n",
    "                       'cnt']],\n",
    "           x=\"mnth\",y=\"cnt\")\n",
    "ax.set(title=\"Monthly distribution of counts\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.5 天气和骑车数目的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5,1,u'weathersit distribution of counts')]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a19add110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "sn.barplot(data=train[['weathersit',\n",
    "                       'cnt']],\n",
    "           x=\"weathersit\",y=\"cnt\")\n",
    "ax.set(title=\"weathersit distribution of counts\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.6 工作日和节假日的分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x113f1f790>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x113eefe10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,(ax1,ax2) = plt.subplots(ncols=2)\n",
    "sn.barplot(data=train,x='holiday',y='cnt',ax=ax1)\n",
    "sn.barplot(data=train,x='workingday',y='cnt',ax=ax2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.7 数值型特征和y之间的相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1958ae50>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aNEhNmjTRmDFjdOrUKQUEBKhr166qX7++nJycdOzYMd26dUvOzs6aO3euSZ+n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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1452ebd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrMatt = train[[\"temp\",\"atemp\",\n",
    "                  \"hum\",\"windspeed\",\n",
    "                  \"casual\",\"registered\",\n",
    "                  \"cnt\"]].corr()\n",
    "mask = np.array(corrMatt)\n",
    "mask[np.tril_indices_from(mask)] = False\n",
    "sn.heatmap(corrMatt, mask=mask,\n",
    "           vmax=.8, square=True,annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "体感温度和温度高度相关\n",
    "目标cnt与温度正相关、湿度和风速负相关"
   ]
  }
 ],
 "metadata": {
  "_change_revision": 0,
  "_is_fork": false,
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
